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Teradata
AI Engineer
Teradata
Hyderabad•Hybrid•2-5 Yrs•Jun 17
Job description
Hybrid
Overview
Key Responsibilities
Responsibilities
- As an AI Engineer, you will contribute to the design, development, and delivery of AI powered services and applications within Teradata’s AI platform ecosystem. You will work closely with senior engineers, AI/ML specialists, and product teams to build scalable, reliable systems leveraging modern AI technologies.
- This role is ideal for engineers who are hands on, eager to learn, and excited to work on production grade AI systems in a cloud native environment.
- Apply AI/ML technologies such as machine learning models, LLMs, embeddings, and basic agentic or RAG workflows under guidance from senior engineers.
- Design, develop, and maintain high quality UI and backend services and applications that support AI driven products and platforms.
- Implement and integrate RESTful APIs with a focus on security, scalability, and maintainability.
- Collaborate with cross functional teams (engineering, AI/ML, product, and platform) to deliver end to end features.
- Write clean, testable code and participate in code reviews to maintain high engineering standards.
- Assist in troubleshooting, debugging, and resolving production issues.
- Learn and follow best practices for cloud native development, reliability, and observability.
- Stay current with emerging trends in AI, machine learning, and software engineering.
- Provide post-production support
Qualifications
- Bachelor’s degree in Computer Science, Engineering, or a related field (or equivalent practical experience).
- 2+ years of experience in software engineering, User Interface, backend development, JavaScript, Angular
- Proficiency in Python, Java, or Go.
- Translating user experience design into code using Angular, Angular Material, NodeJS, strong fundamental knowledge of Object-oriented JavaScript, SASS, CSS3
- Knowledge of advanced JavaScript and browser optimizations
- Basic understanding of distributed systems, APIs, and backend service design.
- Familiarity with software development best practices: version control, testing, and code reviews.
- Exposure to AI/ML concepts, including machine learning pipelines or LLM based systems.
- Familiarity with embeddings, vector databases, or retrieval augmented generation (RAG).
- Interest in agentic AI, automation, or developer productivity tooling.